AI blood test can detect over 50 types of cancer

US researchers have developed a blood test that can accurately detect more than 50 types of cancer and identify in which tissue the cancer originated, often before there are any clinical signs or symptoms of the disease. It has been described in the journal Annals of Oncology.

Tumours shed DNA into the blood, and this contributes to what is known as cell-free DNA (cfDNA). However, as the cfDNA can come from other types of cells as well, it can be difficult to pinpoint cfDNA that comes from tumours. The new blood test analyses chemical changes to the DNA called ‘methylation’ that usually control gene expression. Abnormal methylation patterns and the resulting changes in gene expression can contribute to tumour growth, so these signals in cfDNA have the potential to detect and localise cancer.

The blood test targets approximately one million of the 30 million methylation sites in the human genome. A machine learning classifier (an algorithm) was used to predict the presence of cancer and the type of cancer based on the patterns of methylation in the cfDNA shed by tumours. The classifier was trained using a methylation database of cancer and non-cancer signals in cfDNA. The database is believed to be the largest in the world and is owned by Californian company GRAIL, which funded the study.

“Our earlier research showed that the methylation approach outperformed both whole-genome and targeted sequencing in the detection of multiple deadly cancer types across all clinical stages, and in identifying the tissue of origin,” said Dr Michael Seiden, President of The US Oncology Network and senior author of the paper. “It also allowed us to identify the most informative regions of the genome, which are now targeted by the refined methylation test that is reported in this paper.”

Blood samples from over 4000 participants were used for training and validating the machine learning classifier as part of the Circulating Cell-free Genome Atlas (CCGA) study — 3052 in the training set (1531 with cancer, 1521 without cancer) and 1264 in the validation set (654 with cancer and 610 without cancer). The algorithm analysed these blood samples to identify methylation changes and to classify the samples as cancer or non-cancer, and to identify the tissue of origin.

The researchers found that the classifier’s performance was consistent in both the training and validation sets, with a false positive rate of 0.7% in the validation set, meaning that less than 1% of people would be wrongly identified as having cancer. As a comparison, about 10% of women are wrongly identified as having cancer in national breast cancer screening programs, although this rate can be higher or lower depending on the number and frequency of screenings and the type of mammogram performed.

The classifier’s ability to correctly identify when cancer was present (the true positive rate) was also consistent between the two sets. In 12 types of cancer that are often the most deadly (anal, bladder, bowel, oesophageal, stomach, head and neck, liver and bile duct, lung, ovarian and pancreatic cancers, lymphoma, and cancers of white blood cells such as multiple myeloma), the true positive rate was 67.3% across clinical stages I, II and III. These 12 cancers account for about 63% of cancer deaths each year in the US and, at present, there is no way of screening for the majority of them before symptoms show. The true positive rate was 43.9% for all cancer types in the study across the three clinical stages.

Detection improved with each cancer stage. In the 12 pre-specified cancers, the true positive rate was 39% in stage I, 69% in stage II, 83% in stage III and 92% in stage IV. In all of more than 50 cancer types, the corresponding rates were 18%, 43%, 81% and 93%, respectively. The test was also consistent between the training and validation sets in its ability to identify the tissue where cancer had originated, with an accuracy of 93% in the validation set.

“These data support the ability of this targeted methylation test to meet what we believe are the fundamental requirements for a multicancer early detection blood test that could be used for population-level screening: the ability to detect multiple deadly cancer types with a single test that has a very low false positive rate, and the ability to identify where in the body the cancer is located with high accuracy to help healthcare providers to direct next steps for diagnosis and care,” Dr Seiden said.

“Considering the burden of cancer in our society, it is important that we continue to explore the possibility that this test might intercept cancers at an earlier stage and, by extension, potentially reduce deaths from cancers for which screening is either not available or has poor adherence. To our knowledge, this is the largest clinical genomics study, in participants with and without cancer, to develop and validate a blood test for early detection of multiple cancers.”

Researchers are continuing to validate the test in large, prospective studies in the USA (STRIVE and PATHFINDER studies) and the UK (SUMMIT study), and to examine its feasibility for screening populations.

Image credit: ©iStockphoto.com/Eraxion

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